docs(uiux): UIUX 设计方案大改 + 5 份作业指导书对齐 + 开发任务文档
- UIUX 文档：填充 19 个缺口（多主体画像/健康度/AI+看板/增长域/洞察域/创始人端/OODA/助推/商密） - UIUX 文档：插入 6 个新章节（十四~十九），旧章节重编号为二十~三十一，更新目录和交叉引用 - 作业指导书 x5：导航改为 6 域分组，新增 Context Bar/工作模式/Insight Rail/决策线程/多工作区等 UI 概念 - 新建 docs/2-task-uiux.md：50 个代码落地开发任务，按 P0-P6 分优先级 + 8 Sprint 规划 - 后端/前端：大量新增模型、路由、组件（来自之前 Phase 开发）
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"""Phase 3-4 AI Agent Service 层测试。
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测试所有依赖 LLM 的 AI Agent service：
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- synergy_matcher: 协同匹配
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- alpha_attribution: Alpha 归因
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- exit_predictor: 退出预测
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- aar_agent: AAR 复盘
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- digital_twin_engine: 数字孪生
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- milestone_agent: 里程碑路径切换
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- talent_agent: 人才流动预测
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- board_agent: 董事会摘要
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- innovation_discoverer: 组合创新
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- knowledge_graph_builder: 知识图谱
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- agent_orchestrator: Agent 编排
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- portfolio_rebalancer: 组合再平衡（纯计算，无 LLM）
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"""
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import pytest
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from app.services.synergy_matcher import match_synergy
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from app.services.alpha_attribution import attribute_alpha
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from app.services.exit_predictor import predict_exit
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from app.services.aar_agent import generate_aar, check_aar_triggers
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from app.services.digital_twin_engine import build_twin_model, simulate_scenario
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from app.services.milestone_agent import suggest_path_switch
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from app.services.talent_agent import predict_talent_flow, recommend_talent
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from app.services.board_agent import generate_meeting_summary, generate_questions
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from app.services.portfolio_rebalancer import (
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calculate_marginal_return,
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rebalance_portfolio,
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run_monte_carlo,
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)
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from app.services.agent_orchestrator import orchestrate_agent
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class TestSynergyMatcher:
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"""协同匹配 Agent 测试。"""
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@pytest.mark.asyncio
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async def test_match_synergy_returns_list(self):
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"""match_synergy 应返回 list。"""
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result = await match_synergy("企业A需要数据标注", "企业B有数据标注团队")
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assert isinstance(result, list)
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@pytest.mark.asyncio
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async def test_match_synergy_non_empty(self):
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"""有匹配条件时应返回非空列表。"""
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result = await match_synergy("企业A需要AI算力", "企业B有GPU集群")
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assert len(result) >= 1
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assert "type" in result[0]
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class TestAlphaAttribution:
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"""Alpha 归因 Agent 测试。"""
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@pytest.mark.asyncio
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async def test_attribute_returns_dict(self):
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"""attribute_alpha 应返回 dict。"""
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result = await attribute_alpha(
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{"type": "战略建议", "description": "调整产品方向"},
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{"revenue": "+20%", "users": "+15%"},
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)
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assert isinstance(result, dict)
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@pytest.mark.asyncio
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async def test_attribute_has_fields(self):
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"""归因结果应包含关键字段。"""
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result = await attribute_alpha({"type": "人才引进"}, {"headcount": "+10"})
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assert "score" in result or "confidence" in result or "summary" in result
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class TestExitPredictor:
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"""退出预测 Agent 测试。"""
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@pytest.mark.asyncio
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async def test_predict_exit_returns_dict(self):
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"""predict_exit 应返回 dict。"""
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result = await predict_exit("企业估值5亿，年收入1亿，增长率30%")
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assert isinstance(result, dict)
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@pytest.mark.asyncio
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async def test_predict_exit_non_empty(self):
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"""退出预测结果应非空。"""
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result = await predict_exit("企业估值5亿，年收入1亿，增长率30%")
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assert len(result) > 0
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class TestAARAgent:
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"""AAR 复盘 Agent 测试。"""
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@pytest.mark.asyncio
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async def test_generate_aar_returns_dict(self):
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"""generate_aar 应返回 dict。"""
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result = await generate_aar(
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"融资失败",
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"计划Q2完成A轮融资5000万",
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"仅获得2000万意向，未达成目标",
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)
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assert isinstance(result, dict)
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@pytest.mark.asyncio
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async def test_check_aar_triggers_funding(self):
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"""融资完成事件应触发 AAR。"""
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events = [
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{"type": "funding_completed", "title": "A轮融资完成"},
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{"type": "normal_event", "title": "例会"},
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]
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triggers = await check_aar_triggers("company-1", events)
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assert len(triggers) == 1
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assert triggers[0]["trigger_type"] == "funding_completed"
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@pytest.mark.asyncio
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async def test_check_aar_triggers_talent(self):
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"""人才离职事件应触发 AAR。"""
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events = [
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{"type": "talent_left", "title": "CTO离职"},
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]
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triggers = await check_aar_triggers("company-1", events)
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assert len(triggers) == 1
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assert triggers[0]["trigger_type"] == "talent_left"
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@pytest.mark.asyncio
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async def test_check_aar_triggers_empty(self):
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"""无触发事件时应返回空列表。"""
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events = [
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{"type": "normal_event", "title": "日常会议"},
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]
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triggers = await check_aar_triggers("company-1", events)
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assert triggers == []
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class TestDigitalTwinEngine:
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"""数字孪生 Agent 测试。"""
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@pytest.mark.asyncio
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async def test_build_twin_model(self):
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"""build_twin_model 应返回 dict。"""
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result = await build_twin_model("企业A：年收入1亿，团队50人，产品SaaS")
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assert isinstance(result, dict)
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@pytest.mark.asyncio
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async def test_simulate_scenario(self):
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"""simulate_scenario 应返回 dict。"""
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result = await simulate_scenario(
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{"revenue_growth": 0.2, "burn_rate": 500000},
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"市场下行20%",
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)
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assert isinstance(result, dict)
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class TestMilestoneAgent:
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"""里程碑 Agent 测试。"""
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@pytest.mark.asyncio
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async def test_suggest_path_switch(self):
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"""suggest_path_switch 应返回 dict。"""
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result = await suggest_path_switch(
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"当前里程碑：产品MVP完成",
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"竞品提前发布，市场窗口缩小",
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)
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assert isinstance(result, dict)
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class TestTalentAgent:
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"""人才 Agent 测试。"""
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@pytest.mark.asyncio
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async def test_predict_talent_flow(self):
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"""predict_talent_flow 应返回 dict。"""
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result = await predict_talent_flow("企业A：50人，近期3人离职")
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assert isinstance(result, dict)
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@pytest.mark.asyncio
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async def test_recommend_talent(self):
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"""recommend_talent 应返回 list。"""
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result = await recommend_talent("需要AI算法工程师", "人才池：张三、李四")
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assert isinstance(result, list)
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class TestBoardAgent:
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"""董事会 Agent 测试。"""
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@pytest.mark.asyncio
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async def test_generate_meeting_summary(self):
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"""generate_meeting_summary 应返回 str。"""
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result = await generate_meeting_summary("Q3财报：收入增长20%，但利润下降")
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assert isinstance(result, str)
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assert len(result) > 0
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@pytest.mark.asyncio
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async def test_generate_questions(self):
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"""generate_questions 应返回 list。"""
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result = await generate_questions("Q3财报：收入增长20%，但利润下降")
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assert isinstance(result, list)
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class TestPortfolioRebalancer:
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"""组合再平衡计算测试（纯计算，无 LLM 依赖）。"""
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def test_calculate_marginal_return_positive(self):
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"""正投资边际回报率应正确计算。"""
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result = calculate_marginal_return(1000000, 500000, 0.2)
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assert result > 0
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assert result == 0.2 # 边际回报率 = 期望回报率
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def test_calculate_marginal_return_zero_investment(self):
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"""追加投资为 0 时边际回报率应为 0。"""
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result = calculate_marginal_return(1000000, 0, 0.2)
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assert result == 0.0
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def test_rebalance_portfolio(self):
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"""再平衡应按边际回报率排序。"""
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companies = [
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{"company_id": "c1", "marginal_return": 0.05},
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{"company_id": "c2", "marginal_return": 0.15},
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{"company_id": "c3", "marginal_return": 0.10},
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{"company_id": "c4", "marginal_return": 0.02},
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]
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result = rebalance_portfolio(companies)
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assert "reallocation_plan" in result
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assert "increase" in result["reallocation_plan"]
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assert "decrease" in result["reallocation_plan"]
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# c2 (0.15) 应在 increase 中
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assert "c2" in result["reallocation_plan"]["increase"]
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# c4 (0.02) 应在 decrease 中
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assert "c4" in result["reallocation_plan"]["decrease"]
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def test_rebalance_empty(self):
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"""空列表应返回空计划。"""
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result = rebalance_portfolio([])
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assert result["reallocation_plan"]["increase"] == []
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assert result["reallocation_plan"]["decrease"] == []
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def test_run_monte_carlo_with_floats(self):
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"""纯 float 列表应正常模拟。"""
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result = run_monte_carlo([0.15, 0.10, 0.05], iterations=100)
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assert "irr_distribution" in result
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assert "percentile_p5" in result
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assert "percentile_p50" in result
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assert "percentile_p95" in result
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def test_run_monte_carlo_with_dicts(self):
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"""dict 列表（含 irr 字段）应正常模拟。"""
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result = run_monte_carlo(
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[{"company_id": "c1", "irr": 0.15}, {"company_id": "c2", "irr": 0.08}],
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iterations=100,
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)
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assert "irr_distribution" in result
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assert result["percentile_p50"] > 0
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def test_run_monte_carlo_empty(self):
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"""空列表应返回零值。"""
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result = run_monte_carlo([], iterations=100)
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assert result["percentile_p5"] == 0
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assert result["percentile_p50"] == 0
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assert result["percentile_p95"] == 0
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class TestAgentOrchestrator:
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"""Agent 编排测试。"""
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@pytest.mark.asyncio
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async def test_orchestrate_low_autonomy(self):
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"""低自治级别应标记需要人工审核。"""
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result = await orchestrate_agent("test_agent", "low", {"task": "分析"})
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assert isinstance(result, dict)
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assert "needs_review" in result or "autonomy_level" in result or "status" in result
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@pytest.mark.asyncio
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async def test_orchestrate_high_autonomy(self):
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"""高自治级别应直接执行。"""
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result = await orchestrate_agent("test_agent", "high", {"task": "生成报告"})
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assert isinstance(result, dict)
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